Policy and market forces delay real estate price declines on the US coast

Despite increasing risks from sea-level rise (SLR) and storms, US coastal communities continue to attract relatively high-income residents, and coastal property values continue to rise. To understand this seeming paradox and explore policy responses, we develop the Coastal Home Ownership Model (C-HOM) and analyze the long-term evolution of coastal real estate markets. C-HOM incorporates changing physical attributes of the coast, economic values of these attributes, and dynamic risks associated with storms and flooding. Resident owners, renters, and non-resident investors jointly determine coastal property values and the policy choices that influence the physical evolution of the coast. In the coupled system, we find that subsidies for coastal management, such as beach nourishment, tax advantages for high-income property owners, and stable or increasing property values outside the coastal zone all dampen the effects of SLR on coastal property values. The effects, however, are temporary and only delay precipitous declines as total inundation approaches. By removing subsidies, prices would more accurately reflect risks from SLR but also trigger more coastal gentrification, as relatively high-income owners enter the market and self-finance nourishment. Our results suggest a policy tradeoff between slowing demographic transitions in coastal communities and allowing property markets to adjust smoothly to risks from climate change.

This manuscript presents the Coastal Home Ownership Model, which uses an agent-based modeling approach to simulate the feedbacks in the coupled human-natural system in coastal communities experiencing emerging flood exposure due to sea level rise.The model is applied to examine how owners, renters, and investors value coastal property and invest in coastal management under various policy scenarios.Results indicate that certain management policies, such as providing subsidies for beach nourishment and tax advantages for high-income owners, will dampen and delay the effect of sea level rise on property values in coastal communities.Removing subsidies will allow property values to more accurately reflect the risk due to sea level rise but will also lead to a transition to wealthier ownership along the coast, pushing out lower-income owners.This highlights an interesting trade-off in the management of coastal property markets, with important economic and equity implications in how coastal communities respond to sea level rise.Overall, the manuscript is well-written, clearly organized, and addresses a topic of current interest to academics and practitioners across multiple fields.There are a few points of clarification that should be addressed before recommending the article for publication.Line numbers were not provided, so I will do my best to be clear about the relevant locations in the text.

Major comments:
• The introduction should include some review of the literature on coupled human-natural systems modeling and clearly distinguish what advances are achieved through C-HOM.This would enable the reader to better understand the novelty of the work.
• The manuscript presents three scenarios that explore how changes from the baseline will influence the feedbacks observed in the system (page 6, paragraph 1).However, no rationale was provided to support the choice of the values for the beach nourishment subsidy and the timing/magnitude of outside market appreciation/depreciation. I think the paper would benefit from a sensitivity analysis that explores how changes in the choice of these values influences the system trajectory.
• Certain modeling components should be explained in greater detail to enable the reader to evaluate the approach and understand potential limitations.For example: o How is the volume of sand (and thus the price of nourishment) determined over time?o Does beach nourishment have any effect on the level of exposure (i.e., can a wider beach reduce some flooding impact)?Or is beach nourishment only viewed from an amenity perspective?If the latter is true, which I believe is the case, is it possible to incorporate hazard reduction due to nourishment in the risk premium formulation?o A nourished dune height is listed in Table S1, but I don't recall this being defined in the model.Please clarify if dune height is considered in the model.o Please provide an explanation of how the scaling parameters (a1 and a2) were determined.

Minor comments:
• Page 4, paragraph 1: Please provide a definition for a "bubble" in the context of this paper.While this term is widely used, I think a clear explanation would be helpful for the general reader.
• Page 4, paragraph 5: Why is a 150-year time horizon chosen for this analysis?• Page 6, paragraph 2: Is the "barrier height" a reference to the barrier island itself?Or is there assumed to be another structural barrier providing flood protection?Please clarify.• Page 17, paragraph 1: How is the spatial extent of the nourishment unit determined?For this hypothetical case, why is it important that it be "smaller than a town but larger than a census block group"?• Page 18, first line: Include "equation" before the number 4.
• Page 20, equation 13: Please define all terms that are not previously defined.I think a3 should be a2, as referenced in Table S1.

This paper develops the Coastal Home Ownership Model (C-HOM) to evaluate the long-term effects of sea level rise (SLR) on coastal real estate markets. C-HOM integrates several important features of real
estate markets, physical coastal systems, and their interacfions to explain how changes in the real estate markets and SLR mifigafion strategies (in this case, beach nourishment) might support (or delay the eventual collapse) of coastal housing markets.The method combines evidence/methods from several strands of the exisfing literature to model the human-physical integrated system.
We thank the reviewer for providing construcfive comments.Below we repeat the reviewer's comments in italics with our responses in plain text.We believe that addressing these comments has strengthened the clarity and the contribufion of the paper.This is a valuable exercise demonstrafing how real estate markets may not reflect SLR risk due to endogenous changes in investment behavior and demographic characterisfics.My main substanfive comment relates to the feature of the model that housing supply is fixed and the demand is the driver of prices.In the long run, it seems that supply is another reasonable margin of adjustment.The authors jusfify that coastal communifies are often built back after structures are destroyed, but might this decision be endogenous to impending SLR and the changing environment as well?If it is out of scope to build this into the current framework, then perhaps some discussion of its impacts would be useful.
The reviewer is correct that the model fixes the housing supply, and it is beyond our scope to endogenize the housing supply.This assumpfion is jusfified by our focus on barrier island communifies in the Southeastern U.S., which are built out (and have been for some fime).What happens when inundafion forces the housing supply to shrink substanfially is speculafive, and our model is primarily used to probe the period leading up to that eventuality.We now acknowledge this limitafion in the discussion secfion where we point to endogenizing housing supply as important future work.
We add the following text to the introducfion to clarify: The barrier island communifies in our model consfitute a natural boundary to define coastal housing markets and to measure the extent of the impact of local adaptafion measures.On developed coastal barrier islands, oceanfront and near shore housing markets are often fully developed and changes in housing supply tend to reflect damage to property and building back homes after hurricanes (Lazarus et al 2018).In a recent empirical study of coastal development on the southern barrier islands in North Carolina, only 8% of parcels were newly developed between 1993 and 2013 (Li, Gopalakrishnan, and Klaiber 2023), with a smaller percentage of developable oceanfront parcels.Holding housing supply fixed in the model allows us to examine demand-driven market dynamics without loss of generality.

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Then we add the following text to the discussion secfion: Although the barrier island communifies that mofivate our modeling are largely built out and have been for decades, allowing the housing supply to adjust upward or downward would be a useful generalizafion for future work.Endogenizing housing supply would enable C-HOM to analyze a wider range of coastal communifies.
A second comment relates to the values used for model inputs.For some model parameters, the range of appropriate values may be wider, e.g., the discount rate.Is there anything interesfing that might happen with the predicfions when varying these values or allowing for heterogeneity here?
In response to this comment and the comments of the other reviewers, we conduct a number of sensifivity analyses that are included in the supplementary materials.We also include a new annotated parameter table in which the parameters and parameter ranges are more fully documented and that points to the cases in which we run sensifivity analyses.In general, we find that the qualitafive conclusions from the baseline model runs and the three scenario experiments that we run are unchanged.What different parameter values do is to alter the magnitudes of some of the changes and alter the fiming of changes, e.g.how long the decline in property value is delayed after the onset of SLR.For the discount rate example specifically, we used 6%, which is used in the beach nourishment simulations in Gopalakrishnan et al. (2011).The average 30-year nominal mortgage rate from 1971-2023 is 7.74% based on 30-Year Fixed Rate Mortgage Average in the United States, Percent, Weekly, Not Seasonally Adjusted.The average rate of inflation in this time is 4%, suggesting a real mortgage rate close to 3.75%.To explore lower (and potentially higher) rates, we run models using discount rates of 3% and 9%.
We add the following text and figures to the supplemental: We re-run the nourishment policy experiment using a low discount rate (3%) and a high discount rate (9%).Compared to our findings in the main text, nothing changes qualitatively.We see that the decline in property value begins later for the low discount rate case.This timing difference is due to the fact that, with the lower discount rate, there is more room in the market for high-income owners to flux in (because they are less tax advantaged compared to the cases with higher discount rates).
Low Discount Rate -Reduced Subsidy 3 High Discount Rate -Reduced Subsidy To ensure that the mechanism is working as we expect, we also run the low and high discount rate cases relative to the baseline 6% discount case.Here we see clearly that, with the low discount rate, property values are slightly higher before the onset of SLR and more so for non-oceanfront because they are taxed less to fund nourishment.Before SLR, owners are lower-income relative to the baseline because the lower discount rate creates less tax advantage (recall discount rate is multiplied by marginal tax rate).After the onset of SLR, wealthier owners flux in and drive prices up further.The opposite is true in the high discount rate case -lower prices due to less capitalization and more tax advantage for high-income owners so fewer high-income owners who can flux in later.
Low Discount Rate Case -Compared to Baseline 4 High Discount Rate Case

Pg. 3: "Empirical data are also necessarily limited by experiences in the past and may not reflect future scenarios." I am not enfirely clear what this means. Do you mean that the individual behaviors generafing data might be backward looking (e.g., as in your assumpfions of expectafions for beach width and capital gains) rather than forward looking?
We reworded to clarify and replaced that sentence with these two: The range of experience of SLR and storm risk captured in empirical studies may not include the full range of possibilifies under future scenarios.For example, if risks respond nonlinearly as SLR progresses beyond what has been observed in the past, only modeling studies are capable of exploring the implicafions.
There are some typographical errors throughout the manuscript that should be corrected.
After revising the text, we went through to check two addifional fimes.Thank you again for these comments.

Reviewer #2: This paper introduces the Coastal Home Ownership Model (C-HOM) and conducts an in-depth analysis of the long-term evolufion of coastal real estate markets.
We thank the reviewer for providing construcfive comments.Below we repeat the reviewer's comments in italics with our responses in plain text.We believe that addressing these comments has strengthened the clarity and the contribufion of the paper.

Key Points for Considerafion:
1. Structural Order: The paper's structural arrangement could benefit from reordering.It would be more logical and enhance readability if the Model secfion is reposifioned to precede the Results and Conclusion secfions.
We appreciate this comment, and we are happy to make this change if the Editor allows it.However, the journal specificafions place Methods at the end, and our model descripfion is the Methods secfion.Specifically, the journal website says: "The main text of an Arficle should begin with a secfion headed Introducfion of referenced text that expands on the background of the work (some overlap with the abstract is acceptable), followed by secfions headed Results, Discussion (if appropriate) and Methods (if appropriate)."Yes, this ordering is not the most common way to structure a paper, but it is not unprecedented in other high-impact journals.For example, PNAS uses this order.1 with real-world data.This validafion process helps ensure that the model accurately mirrors real-world scenarios.

Parameter Validafion: An essenfial aspect of assessing the validity of the C-HOM model is comparing the parameter values presented in Table
In response to this comment and the comments of the other reviewers, we include a new annotated parameter table in which the parameter values and parameter ranges are more fully documented and that points to cases in which we run sensifivity analyses.The sensifivity analyses that are included in the supplementary materials and focus on parameter for which we have less ability to pin them down empirically.In general, we find that the qualitafive conclusions from the baseline model runs and the three scenario experiments that we run are unchanged.What different parameter values do is to alter the magnitudes of some of the changes and alter the fiming of changes, e.g.how long the decline in property value is delayed after the onset of SLR.
While further details are in the Supplemental Material, we highlight two examples here: 1) the discount rate, which is nearly always an important parameter in dynamic economic analyses and 2) the flux parameter, which is difficult to measure and for which we thus consider a wide range.For the discount rate example specifically, we used 6%, which is used in the beach nourishment simulations in Gopalakrishnan et al. (2011).Alternatively, the average 30-year nominal mortgage rate from 1971-2023 is 7.74% based on the 30-Year Fixed Rate Mortgage Average in the United States, Percent, Weekly, Not Seasonally Adjusted.The average rate of inflation in this time is 4% (based on the U.S. CPI All Urban Consumers), suggesting a real mortgage rate close to 3.75%.To explore lower (and potentially higher) rates, we run models using discount rates of 3% and 9%.To this end, see if reducing the nourishment subsidy has the same effect.Compared to our findings in the initial submission, nothing changes qualitatively.We see that the decline in property value begins later for the low discount rate case.This is due to the fact that, with the lower discount rate, there is more room in the market for high-income owners to flux in (because they are less tax advantaged compared to the cases with higher discount rates).

Low Discount Rate -Reduced Subsidy High Discount Rate -Reduced Subsidy
To ensure that the mechanism is working as we expect, we also run the low and high discount rate cases relative to the baseline.So, here you can see clearly that with the low discount rate, property values are slightly higher before the onset of SLR and more so for non-oceanfront because they are taxed less to fund nourishment.Before SLR, owners are lower-income relative to the baseline because the lower discount rate creates less tax advantage (recall discount rate is multiplied by marginal tax rate).After the onset of SLR, wealthier owners flux in and drive prices up further.The opposite is true in the high discount rate case -lower prices due to less capitalization and more tax advantage for high-income owners so fewer high-income owners who can flux in later.

Low Discount Rate Case -Compared to Baseline High Discount Rate Case
To examine to the sensifivity of the model to the flux parameter, we highlight the speed of adjustment under two flux values.The first (4x baseline) leads to rapid adjustment in which the property value adjusts almost instantaneously to outside markets (within 2 years).The second (0.1x baseline) is a slow adjustment that unfolds over 30 years.These two extremes-unrealisfically short or unrealisfically long speed of adjustment-provide jusfificafion our baseline flux parameter choice.

Local Market Dynamics:
Recognizing the localized nature of real estate markets, it is crucial to address how the C-HOM model accounts for regional variafions.Incorporafing these local differences is essenfial for the model's applicability across diverse markets.This is an important point for robustness of our conclusions.We explore this issue in two dimensions: 1) the base parameter in the willingness-to-pay funcfion; and 2) the rate of sea-level-rise.We add the following texts to the supplemental: The base parameter (alpha) captures the variafion in the background strength of the real estate market.A higher value means higher-value homes, typically associated with proximity to stronger labor markets, befter schools, lower crime, and other environmental amenifies that we are not explicitly modeling.Varying the rate of SLR accounts for the fact that SLR has been documented to be heterogeneous along the coast (Pecuch et al. 2018).We allow the base parameter to increase or decrease and the rate of SLR to increase and re-run the model for all four combinafions.First, we show how these changes do not affect the qualitafive conclusions of the nourishment policy experiment.Second, we compare each case to the baseline to ensure that the mechanisms are working as we expect.

Scenarios Decreasing the Nourishment Subsidy
Low Alpha -base SLR High Alpha -base SLR Low Alpha -high SLR High Alpha -high SLR In the low alpha case, results are qualitafively the same as the main text findings.The difference is that property values can be sustained for longer because fewer high-income owners have entered the market at the onset of SLR.In the high alpha case, results are similar in that the subsidy only temporarily maintains property values in the face of SLR, but there is a shorter period in which high-income owners can enter before saturafing the market.At first, the paftern of nourishment remains the same compared to the nourishment policy experiment in the main text.However, after roughly 30 years, an addifional nourishment cycle begins, which the higher base property value jusfifies.So, we see some recovery in property values and beach width relafive to the baseline 90% subsidy case.These suggesfions aim to enhance the paper's overall clarity, validity, and applicability, contribufing to a more comprehensive explorafion of coastal real estate market dynamics.

Scenarios
Thank you again for these comments.

Reviewer #3 (Remarks to the Author):
This manuscript presents the Coastal Home Ownership Model, which uses an agent-based modeling approach to simulate the feedbacks in the coupled human-natural system in coastal communifies experiencing emerging flood exposure due to sea level rise.The model is applied to examine how owners, renters, and investors value coastal property and invest in coastal management under various policy scenarios.Results indicate that certain management policies, such as providing subsidies for beach nourishment and tax advantages for high-income owners, will dampen and delay the effect of sea level rise on property values in coastal communifies.Removing subsidies will allow property values to more accurately reflect the risk due to sea level rise but will also lead to a transifion to wealthier ownership along the coast, pushing out lower-income owners.This highlights an interesfing trade-off in the management of coastal property markets, with important economic and equity implicafions in how coastal communifies respond to sea level rise.Overall, the manuscript is well-wriften, clearly organized, and addresses a topic of current interest to academics and pracfifioners across mulfiple fields.There are a few points of clarificafion that should be addressed before recommending the arficle for publicafion.
We thank the reviewer for providing construcfive comments.Below we repeat the reviewer's comments in italics with our responses in plain text.We believe that addressing these comments has strengthened the clarity and the contribufion of the paper.
Line numbers were not provided, so I will do my best to be clear about the relevant locafions in the text.
We added line numbers for the revision.

Major comments:
• The introducfion should include some review of the literature on coupled human-natural systems modeling and clearly disfinguish what advances are achieved through C-HOM.This would enable the reader to befter understand the novelty of the work.
Although a comprehensive review is beyond the scope of our paper, we add the following three paragraphs.The first is a general review of the coupled human-natural systems literature with a focus on land use change.The second is specifically about the work on coupled coastal systems and what C-HOM adds specifically to this literature.The third is parallel work on causality and coupled systems and why models like C-HOM are important for evaluafing and the empirical literature and mofivafing future empirical work.We also include all of the references below these paragraphs.
This work contributes to advancing the growing literature on coupled human and natural systems.Humans are constantly changing the natural environment surrounding them and understanding dynamic feedback between human behavior natural systems often requires more than just superimposing an economic model on the physical or biological system (Liu et al. 2007).Applications of coupled modeling of dynamic human-natural feedbacks dates back at least to the 1960s when bioeconomic models were used to study the human and natural components of fisheries (Smith 1969;Abbott, Sanchirico, and Smith 2018).The literature expanded dramatically when researchers began using spatially explicit data to study land use and land cover change, urbanization pattens, and to evaluate conservation interventions (Plantinga et al. 1999;Carrion-Flores and Irwin 2004;Polasky et al. 2008;An 2012;Lawler et al. 2014;Plantinga 2015).Progress in understanding coupled human-natural systems adds complexity by modeling non-linear feedbacks between physical processes and human responses across space and time (Liu et al. 2007;Levin et al. 2012).
In coastal systems, the evolution of the coastal-economic zone cannot be understood with methods in economics or coastal modeling alone; rather, it depends on complex interactions between physical coastal systems and economic behavior (McNamara and Werner 2008;McNamara et al. 2015).In these systems, incorporating relatively simple models of human behavior with a detailed geophysical model of coastal evolution (McNamara, Murray, and Smith 2011;Lazarus et al. 2018) and coupling simplified dynamics of coastal change with detailed economic decision-making (Smith et al. 2009;Gopalakrishnan et al. 2017) can generate new insights and emergent patterns in the coupled system (Murray et al. 2013).Adding complexity in any one dimension can reveal system characteristics that may not be consistent with simpler or more complex models.We add to this literature by endogenizing real estate values and demographic changes as functions of SLR risk in a model that also includes model features and couplings from this previous work, namely beach erosion, storm risk, the effects of beach width on property value, and local public finance decisions to rebuild beaches.
In the absence of modeling of a coupled human-natural system, researchers can also misinterpret empirical results and potentially draw the wrong policy implications (Smith 2014;Ferraro, Sanchirico, and Smith 2019;Schlüter et al. 2023).Even in simple models of coupled systems, state variables behave in non-intuitive ways such as being positively correlated over some time intervals and negatively correlated over others (Abbott, Sanchirico, and Smith 2018).As such, there is a growing need to use modeling to evaluate the reliability and plausibility of empirical evidence for causal claims and to elucidate potential mechanisms for surprising empirical findings (Ferraro, Sanchirico, and Smith 2019;Schlüter et al. 2023).The use of coupled systems modeling to inform empirical specifications can also lead to substantially different estimates, such as a value of beach that more than double the estimate that ignores the coupling (Gopalakrishnan et al. 2011).
Abbott, J.K., Sanchirico, J.N. and Smith, M.D., 2018.• The manuscript presents three scenarios that explore how changes from the baseline will influence the feedbacks observed in the system (page 6, paragraph 1).However, no rafionale was provided to support the choice of the values for the beach nourishment subsidy and the fiming/magnitude of outside market appreciafion/depreciafion. I think the paper would benefit from a sensifivity analysis that explores how changes in the choice of these values influences the system trajectory.
Below we provide justification for each of our scenarios with references.The references are a mix of academic papers, grey literature, and news articles about particular places.We now include this information in the supplemental.We also conduct a sensitivity analysis as the reviewer suggests, and our qualitative results are unchanged.Below is a heatmap to summarize some of the sensitivity analyses, looking specifically at the effects of varying parameter values on property values over time.
See the Supplemental Figures for more details that explore changes in the baseline, policy experiments, and variations of our scenarios.

Justification of the Baseline Case -90% Nourishment Subsidy
The 90% baseline captures the typical case.Although there is considerable variation in how beach nourishment projects are funded, local funding from property taxes typically constitutes a small share of the total.For most beach nourishment projects, the federal subsidy has been approximately two thirds of the cost with the remaining one third financed by a combination of indirect federal subsidies for inlet stabilization and dredge disposal, state contributions, hotel taxes, local sales taxes, and local property taxes (NC DENR 2011; Brockbank et al. 2020;Gopalakrishnan, Landry, and Smith 2018;Star News 2021).Local property taxes are the non-subsidized component in the sense that it is paid directly by property owner beneficiaries of the project.Some projects are cost-shared between state, federal, and local funding with others having federal and state cost sharing that covers the entire cost of the project, e.g. in Louisiana (Elko et al. 2021).In some places, such as Kure Beach, NC and Wrightsville Beach, NC, local property taxes do not pay any share of the project (Town of Kure Beach 2019; Town of Wrightsville Beach 2023).Federal, state, and local funding shares vary substantially across projects in South Carolina with local funding providing no contributions in many instances but shouldering the entire burden in others (Houston 2021).Historically, between 65% and 85% of beach nourishment projects have had a federal component (Trembanis, Pilkey, and Valverde 1999).In Figure 4 of Valverde, Trembanis, and Pilkey (1999), 43% is federal storm and erosion, 14% is federal navigation, 6% is federal emergency, 2% is state, 18% is state and local cooperative agreements, and only 9% of funding is classified as local/private with 8 % as unknown.
Justification for Scenario 1 -Nourishment Subsidy Cut from 90% to 50% The reduction to a 50% subsidy is a plausible change based on the political economy over the past two decades during which there has been momentum to reduce the federal share of funding for beach nourishment dramatically, decrease state contributions, and increase the share shouldered by local sources.Both the William Clinton and George W. Bush Administrations proposed cutting the two-thirds federal share in half, although Congress maintained subsidies at a higher level during their administrations (Tampa Bay Times 2019;CentralJersey.com 2001).Cutting the federal share in half alone would reduce the total subsidy to 57%, and there appears to be a similar push to force local communities to shoulder a greater share at the state level (Mullin, Smith, and McNamara 2019;Star News 2021).• Certain modeling components should be explained in greater detail to enable the reader to evaluate the approach and understand potenfial limitafions.For example: o How is the volume of sand (and thus the price of nourishment) determined over fime?
The literature suggests sand costs are about $10 per cubic yard (Gopalakrishnan, Landry, and Smith 2018;Elko et al. 2021;Cutler, Albert, and White 2020).There are also fixed costs for planning and mobilizing equipment that are included in the cots.Sand volume is based on the alongshore length and width of the project (Smith et al. 2009;McNamara et al. 2011) o Does beach nourishment have any effect on the level of exposure (i.e., can a wider beach reduce some flooding impact)?Or is beach nourishment only viewed from an amenity perspecfive?If the lafter is true, which I believe is the case, is it possible to incorporate hazard reducfion due to nourishment in the risk premium formulafion?
We chose not to add this feature because the empirical literature is somewhat ambiguous on the mechanism, and the risk terms in our model are already complicated and not easily parameterized.Quasi-experimental studies that exploit spafial and temporal differences in adaptafion investments and the occurrence of hazard events show that nearshore housing prices capitalize potenfial storm risk reducfion from beach nourishment in North Carolina (Qiu & Gopalakrishnan, 2018) and the construcfion of vegetated dunes along the New Jersey coast (Dundas, 2017).Both of these are already cited in the paper.However, empirical analyses that separate the benefits of erosion control and hazard mifigafion are limited by the reality that nourishment often is done in conjuncfion with dune construcfion.o Please provide an explanafion of how the scaling parameters (a1 and a2) were determined.
Detailed parameter explanafions and documentafion are now included in the notes to Table S1.The table also provides mofivafion for our sensifivity analyses.

Minor comments:
• Page 4, paragraph 1: Please provide a definifion for a "bubble" in the context of this paper.While this term is widely used, I think a clear explanafion would be helpful for the general reader.
We add this definifion: "A bubble occurs when prices rise substanfially above expected prices based on market fundamentals over a short period of fime." • Page 4, paragraph 5: Why is a 150-year fime horizon chosen for this analysis?
We add the following text: "This length of fime allows us to consider longer horizons than a typical 30year mortgage; run the model for 50 years without SLR as a inifial period to understand internal mechanisms in the model; and evaluate large, long-term effects of SLR and changing storm climate (over the subsequent 100 years)." • Page 6, paragraph 2: Is the "barrier height" a reference to the barrier island itself?Or is there assumed to be another structural barrier providing flood protecfion?Please clarify.
Yes, the elevafion of the barrier island.In the text, we added "i.e., elevafion of the barrier island" • Page 17, paragraph 1: How is the spafial extent of the nourishment unit determined?For this hypothefical case, why is it important that it be "smaller than a town but larger than a census block group"?
Typically towns decide on nourishment projects if they are partly funded through property taxes.
However, many towns throughout the U.S. have special municipal tax districts that face different tax rates to fund specific services.This is a common pracfice for beach nourishment.We add the following to the text.

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This spafial extent allows for the decision-making unit for a nourishment project that is funded by property taxes to be a town, but it includes the possibilifies that the town may not nourish its enfire beach and that there are special tax districts within the town that pay higher rates to fund the project (Mullin, Smith, and McNamara 2019).

Done.
• Page 20, equafion 13: Please define all terms that are not previously defined.I think a3 should be a2, as referenced in Table S1.
That's correct.We add to the text here, "where $MSL_t$ is mean sea level, which changes with SLR, and $h^{elev}$ is inifial barrier elevafion."We add more clarificafions in Table S1.
Thank you again for these comments.
Compared to the Baseline Low Alpha -base SLR High Alpha -base SLR Low Alpha -high SLR High Alpha -high SLR When we compare to the baseline, we can clearly see how the mechanism is working.Before SLR, the lower base property decreases property values, and the higher base property increases property values.Lower base property value decreases the average tax rate, and higher base property value increases the average tax rate.With lower alpha, the demographic shift narrows the gap in property value compared to the baseline, whereas with higher alpha, the demographic shift exacerbates the gap.Higher SLR just erodes property values overall relafive to the baseline with lower SLR.To this end, we repeatedly change the seed for the random number generator and re-run the baseline model.We use the resulfing set of simulafions to trace out 95% confidence intervals on the simulafions.Supplemental Fig 8 shows the results.For most variables, the resulfing confidence intervals are quite narrow.The main conclusions of the model are unchanged.
Gopalakrishnan, S., Smith, M.D., Slott, J.M. and Murray, A.B., 2011.The value of disappearing beaches: A hedonic pricing model with endogenous beach width.Journal of EnvironmentalEconomics and  Management, 61(3), , L., 2012.Modeling human decisions in coupled human and natural systems: Review of agent-based models.Ecological modelling, 229, pp.25-36.Carrion-Flores.C, Irwin E.G. 2004.Determinants of residential land use conversion and sprawl at the rural urban fringe.American Journal of Agricultural Economics 86(4):889-904 Common property resources and the dynamics of overexploitation: The case of the north pacific fur seal-A 42-Year Legacy.Marine ResourceEconomics, 33(3),An

Jusfificafion for Scenario 2 -Appreciafion in outside real estate markets, namely a doubling in 50 years.
This scenario is based on historic real (inflation-adjusted) appreciation in national real estate markets in the United States and projecting that this appreciation will continue into the future.Specifically, doubling real prices in 50 years is a conservative projection of continued real estate appreciation from 1987 to the present (the period for which a consistent national real estate index is available).Doubling in 50 years implies a 1.4% appreciation rate.The historical real rate of appreciation in U.S. real estate markets (after adjusting for inflation) is 1.6%.This rate is calculated from the S&P/Case-Shiller U.S. National Home Price Index, Index Jan 2000=100, Monthly, Seasonally Adjusted, which is converted from nominal to real using the Consumer Price Index for All Urban Consumers: All Items in U.S. City Average, Index 1982-1984=100, Monthly, Seasonally Adjusted.The resulting 1.6% is the compounded real rate of appreciation over the period spanning January 1987 through July 2023.

for Scenario 3 -Constant outside real estate markets and then dramafic decline, namely 90% depreciafion in 50 years.
This scenario is exploratory in nature because real estate markets are not guaranteed to appreciate and could depreciate.We chose a substantial long-term depreciation rate to explore specifically if the demographic changes predicted by the model could be reversed.That said, this real rate of depreciation is not outside the rate of depreciation experienced recently on the decadal scale in housing markets.A 90% deprecation over 50 years corresponds to an annual rate of 4.5% depreciation.Based on the S&P/Case-Shiller U.S. Nafional Home Price Index discussed above, between 2006 and 2012, housing markets depreciated at an annual rate of 7%, which is considerably faster than the 4.5% implied by our model.://archive.centraljersey.com/2001/07/05/beach-replenishment-sfill-a-federal-project-presidents-plan-toreduce-funding-overturned-in-the-house-of-representafives/hftps://www.tampabay.com/archive/1999/07/06/costly-beach-proposal-resisted/Town of Wrightsville Beach, NC Beach ManagementPlan.2023.Draft Report, August 14, 2023   Trembanis, A.C., Pilkey, O.H. and Valverde, H.R., 1999.Comparison of beach nourishment along the US Atlanfic, Great Lakes, Gulf ofMexico, and New England shorelines.Coastal Management, 27(4), pp.329-340.Valverde, H.R., Trembanis, A.C. and Pilkey, O.H., 1999.Summary of beach nourishment episodes on the US east coast barrier islands.Journal of Coastal Research, pp.1100-1118. hftps . These parameters are now included and documented in the parameter table.The revised parameter table includes descripfions and jusfificafions for the other parameters as well.Smith, M.D., Slott, J.M., McNamara, D. and Murray, A.B., 2009.Beach nourishment as a dynamic capital accumulation problem.Journal of Environmental Economics and Management, 58(1), pp.58-71.